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Application of double asymptotics and random matrix theory in error estimation of regularized linear discriminant analysis

机译:双渐近和随机矩阵理论在正则线性判别分析误差估计中的应用

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The theory of double asymptotics and random matrices has been employed to construct a nearly unbiased estimator of true error rate of linear discriminant analysis with ridge estimator of inverse covariance matrix in the multivariate Gaussian model. In such a scenario, the performance of the constructed estimator, as measured by Root-Mean-Square (RMS) error, shows improvement over well-known estimators of true error.
机译:利用多元渐近理论和随机矩阵理论,利用多元高斯模型中逆协方差矩阵的岭估计,构造了线性判别分析的真实误差率的近似估计。在这种情况下,构造的估计器的性能(通过均方根(RMS)误差度量)显示出优于已知的真实误差估计器的改进。

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